Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …

Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …

Advances in artificial intelligence technologies have made it possible to obtain more
accurate and reliable results using digital images. Due to the advances in digital …

Background Histopathology image analysis is a gold standard for cancer recognition and
diagnosis. Automatic analysis of histopathology images can help pathologists diagnose …

Breast cancer is the most common malignant disease in women worldwide. In recent
decades, earlier diagnosis and better adjuvant therapy have substantially improved patient …

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

This paper studies the effectiveness of accomplishing high-level tasks with a minimum of
manual annotation and good feature representations for medical images. In medical image …

Multiple-instance learning (MIL) is a recent machine-learning paradigm that is particularly
well suited to medical image and video analysis (MIVA) tasks. Based solely on class labels …

Computer-aided pathology diagnosis based on the classification of Whole Slide Image
(WSI) plays an important role in clinical practice, and it is often formulated as a weakly …

In this paper, we develop a new weakly supervised learning algorithm to learn to segment
cancerous regions in histopathology images. This paper is under a multiple instance …